1 Introduction

The COVID-19 pandemic and its lockdown had significant effects on population health. Changes in lifestyle, such as reduced physical activity, disrupted routines, and increased comfort eating, raised concerns about weight gain and metabolic health, potentially increasing Type 2 diabetes. At the same time, healthcare access was disrupted, with less appointments available, which could have affected the diagnosis and management of chronic conditions.

This report focuses on common Type 2 diabetes medications, including biguanides (metformin), SGLT2 inhibitors (gliflozins), sulphonylureas (gliclazide, glimepiride), DDP-4 inhibitors (gliptins), GLP-1 receptor agonists (glutides), thiazolidinediones (glitazone), and examines how prescribing patterns changed during COVID-19 pandemic across Scotland. This study is motivated by an interest in understanding how the dual pressures of increased chronic disease and barriers to healthcare access during the pandemic influenced Type 2 medication use. It is hypothesized that overall prescribing was disrupted during pandemic, with a potential rebound in prescriptions post-pandemic, and that these changes may have been more pronounced in socioeconomically deprived areas, thereby potentially exacerbating existing health inequalities

For the purpose of this study, the COVID-19 period is defined as March 2020 to June 2021, spanning the introduction of lockdown measures to the lifting of restrictions. The analysis includes all forms of Type 2 diabetes medications recorded in the dataset.

2 Methods

The links were taken from the open data scotland and scottish census for population data.

For the purpose of this study, the COVID-19 period is defined as March 2020 to June 2021, spanning the introduction of lockdown measures to the lifting of restrictions. The analysis includes all forms of Type 2 diabetes medications recorded in the dataset.

library(tidyverse)
library(janitor) # cleaning data
library(gt) # tables
library(here) # directory structure
library(readxl) # read in excel file
library(sf) #to read in map data
library(plotly) #to display information when plot is hovered over
library(scales) #to add colour scheme

#Get data from 24-month period from Mar 2020 - Feb 2022 
#create function to read data in:
prescriptions_data <- function() {
  files <- c(paste0(c("jan", "feb","mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"), "2020"),
             paste0(c("jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"), "2021"))
  
  map_dfr(files, ~read_csv(here("data", paste0(.x, ".csv"))) %>%
                   clean_names() %>%
                   mutate(file_name = .x))}

# Read in data
data_for_years <- prescriptions_data()
deprivation_stats <- read_excel(here("data/SIMD.xlsx"))
#Read in and tidy healthboard population database
population_data <- read_csv(here("data/general_health_census.csv"), skip = 10) %>% 
  filter(!is.na('All people')) %>%  #Removes NA
  rename(hb_name = "General health",
         hb_population = "All people") %>% 
  select(hb_name, hb_population) %>% 
  filter(!str_detect(hb_population, "Cells")) #Removes rows with text in population (to remove copyright info)

3 Results

This graph explored the relationship between….

diabetes_summary <- data_for_years %>%
  #filter for diabetes medications
  filter(!is.na(bnf_item_description)) %>% 
  filter(str_detect(bnf_item_description,
                    regex("metformin|gliptin|gliflozin|glutide|glitazone|gliclazide|glimepiride", ignore_case = TRUE))) %>% 
    group_by(paid_date_month, hbt) %>% 
    summarise(paid_quantity = sum(paid_quantity, na.rm = TRUE)) %>% 
#change months from numbers to letters
    mutate(paid_date_month = ym(paid_date_month))

# Merge all tables together
combined_health_data <- deprivation_stats %>%
  full_join(diabetes_summary, join_by(HBcode == hbt)) %>%
  left_join(population_data, join_by(HBname == hb_name))

# Now calculate prescriptions per person
combined_health_data <- combined_health_data %>%
  group_by(HBname, paid_date_month) %>%
  mutate(quantity_per_head = sum(paid_quantity, na.rm = TRUE) / mean(as.numeric(hb_population))) #calculate prescriptions per person and change hb_population to numeric value instead of character
# Creating a summary table and filtering for common type 2 medications
totalmeds_plot <- combined_health_data %>% 
  ggplot(aes(x = paid_date_month, y = quantity_per_head)) +
  # Add vertical lines for COVID start and end
  geom_vline(xintercept = as.numeric(ymd("2020-03-01")), 
             linetype = "dashed", colour = "orange", size = 0.8) +
  geom_vline(xintercept = as.numeric(ymd("2021-06-01")), 
             linetype = "dashed", colour = "orange", size = 0.8) +
  geom_line(colour = "purple", size = 0.5) + # Make original line lighter
  labs(
    title = "Prescription of type 2 dabetes medications per person",
    subtitle = "January 2020 - December 2021 | Purple dashed lines: COVID period (Mar 2020 - Jun 2021)",
    x = "Month",
    y = "Quantity per head",
  ) +
  theme_light() +
  theme(axis.text.x = element_text(angle = 45, hjust=1)) +
  facet_wrap(~HBname, scales = "free_y")
#labeller = label_wrap_gen(width = 10) ADD TO FACET WRAP

print(totalmeds_plot)

#MENTION FREED SCALES as there were vast differences in prescription across healthboards

An overall upward trend was seen during COVID and this increase accelerated post COVID. Changes in the trend during the COVID perios may be due to the enforcement and lifting of lockdown rules. To further examine and link trends to deprived areas. I will be using January 2020 (baseline), January 2021 (third lockdown), December 2022 (post pandemic/current trend). - I chose these seasons to balance temporal evidence (pandemic stages) and seasonal consistency (comparing winter months avoids confounding by seasonality)

key_months_data <- combined_health_data %>% 
  filter(paid_date_month %in% as.Date(c("2020-01-01", "2021-12-01"))) %>% 
  group_by(HBname) %>% 
  mutate(covid_phase = case_when(
    paid_date_month == as.Date("2020-01-01") ~ "pre_covid",
    paid_date_month == as.Date("2021-12-01") ~ "post_covid"))

# Summarise by health board and COVID phase
lollipop_data <- key_months_data %>%
  group_by(HBname, covid_phase) %>% 
  summarise(avg_qph = mean(quantity_per_head, na.rm = TRUE),
            avg_simd = mean(SIMD2020v2_Decile, na.rm = TRUE)) %>%
  # Pivot to get Pre and Post columns for connecting lines
  pivot_wider(names_from = covid_phase, values_from = avg_qph) %>%
  mutate(percent_change = ((post_covid - pre_covid)/pre_covid)*100) %>% #to calculate percent change
  # Arrange by descending avg_simd
  arrange(desc(avg_simd)) %>%
  ungroup() %>% #before creating a factor so hat HBname can be arranged in order of avg_simd in graph
  # Create factor to preserve ordering in plot
  mutate(HBname = factor(HBname, levels = HBname))

lollipop_graph <- lollipop_data %>% 
  ggplot() +
  geom_segment(aes(x = HBname, xend = HBname, y = pre_covid, yend = post_covid), 
               color = "grey") +
  geom_point(aes(x = HBname, y = pre_covid), 
             color = "orange", size = 3) +
  geom_point(aes(x = HBname, y = post_covid), 
             color = "purple", size = 3) +
  coord_flip() +
  theme_minimal() +
  theme(legend.position = "none") +
  xlab("") +
  ylab("Average Quantity Per Head") +
  ggtitle("Change in Quantity Per Head: Pre-COVID vs Post-COVID",
          subtitle = "NHS Health Boards ordered by average SIMD decile (descending)")

# Display the plot
print(lollipop_graph)

#add text to graph, "click to display name of healthboard"

This shows that Glasgow was the third highestdeprived and has the highest increase in average prescriptions per head. However this is absolute changes for further analysis, the percentage change was calculated.

# load the NHS Health board Shapefile
NHS_healthboards <- suppressMessages(st_read(here("data/Week6_NHS_HealthBoards_2019.shp"), quiet = TRUE)) #reads in file and hides reading in message

#Join spatial data with other health data
summary_map <- NHS_healthboards %>%
  full_join(lollipop_data, by = join_by(HBName == HBname))
# Plot percentage change map
plot_map <- summary_map %>% 
  ggplot() +
  geom_sf(aes(fill = percent_change, text = paste0("Health Board: ", HBName, "\n", round(percent_change,2), "%")), color = "white", size = 0.3) +
  scale_fill_viridis_c(option = "plasma", 
                       name = "% Change in Average Quantity per Head") +
  labs(
    title = "Percentage Change in Average Type 2 Diabetes Prescriptions Per Head \nPre-COVID → Post-COVID",
    caption = "Data from Jan 2020 and Dec 2021"
  ) +
  theme_minimal() +
  theme(legend.position = "right", plot.title = element_text(face = "bold"))

interactive_map <- ggplotly(plot_map, tooltip = "text")

interactive_map

This revealed that the Western Isles had the highest percentage increase which as also part of the Top 5 most deprived areas. However other most deprived areas such as Ayrshire and Arran, Greater Glasgow and Clyde had one of the lowest percentage increases. A Summary table was produced to better display and understand the relationship between deprivation, absolute and percentage increase in avrage type 2 diabetes medication per head.

ranked_table <- lollipop_data %>%  # replace with your dataframe name
  arrange(avg_simd) %>% 
  mutate(Rank = row_number(),
         absolute_change = (post_covid - pre_covid)) %>% 
  select(c(Rank, HBname, pre_covid, absolute_change, percent_change))

# ---- Step 2: GT table ----
ranked_table %>%
  gt() %>%
  cols_label(
    Rank = "Rank (Most to Least Deprived)",
    HBname = "Health Board",
    pre_covid = "Pre-COVID Level (per head)",
    absolute_change = "Absolute Increase",
    percent_change = "Percentage Increase (%)"
  ) %>%
  fmt_number(
    columns = c(pre_covid, absolute_change),
    decimals = 2
  ) %>%
  fmt_number(
    columns = percent_change,
    decimals = 2
  ) %>%
  tab_header(
    title = " Health Boards by Percentage Increase in Type 2 Diabetes Prescriptions",
    subtitle = "Comparing pre-COVID (Jan 2020) to post-COVID (Dec 2021)"
  ) %>% 
#Colour scale for Absolute Increase
  data_color(
    columns = absolute_change,
    method = "numeric",
    palette = "Oranges"
  ) %>%
  # Colour scale for Percentage Increase
  data_color(
    columns = percent_change,
    method = "numeric",
    palette = "Purples"
  )
Health Boards by Percentage Increase in Type 2 Diabetes Prescriptions
Comparing pre-COVID (Jan 2020) to post-COVID (Dec 2021)
Rank (Most to Least Deprived) Health Board Pre-COVID Level (per head) Absolute Increase Percentage Increase (%)
1 Ayrshire and Arran 1,980.79 372.28 18.79
2 Lanarkshire 3,034.89 540.24 17.80
3 Greater Glasgow and Clyde 4,991.68 960.94 19.25
4 Western Isles 107.14 31.07 29.00
5 Dumfries and Galloway 700.08 125.86 17.98
6 Fife 1,524.35 274.49 18.01
7 Tayside 1,526.62 321.51 21.06
8 Highland 1,289.34 302.68 23.48
9 Forth Valley 1,454.79 302.45 20.79
10 Borders 468.36 93.69 20.00
11 Orkney 97.10 18.69 19.25
12 Lothian 2,941.78 714.20 24.28
13 Shetland 87.49 12.64 14.44
14 Grampian 2,040.41 463.12 22.70

4 Conclusion

HOVER OVER MAP SHOULD SAY HEALTHBOARD NAME

Questions:

  1. In feedback what do you mean by showing links for reproducibility? Is code enough? Or do we need to show/write how we got to the links? #### Write overall urls

How do I make figures wider so it doesn’t cut off names?